Revolutionizing Network Security with Hybrid Deep Learning Models for Intrusion Detection

Abstract

Metaphorically speaking, network security in this fast-moving and ever-changing digital sea has become a necessity. In this work, we provide an overview of the use of hybrid deep learning models for intrusion detection, covering some benchmark datasets (including NSL-KDD, CICIDS2019, BoT-IoT and KDDCup99). DSSTE and Conditional GANs help to overcome the data imbalance problem, while Swin Transformers and Seq2Seq models use sophisticated feature extraction methods for accurate spatial and temporal analysis. The hybrid CNN Transformer network which we propose achieves state of the art detection rate for all kinds of attack, namely DoS, DdoS with minimal precision, recall and F1-scores. Besides, issues such as false positives also exist; however, the research shows that hybrid deep learning models have the potential to reformulate intrusion detection systems from passive entities to adaptive, scalable, and proactive units for next-generation network-level attacks